Mathematical modeling and analysis for the co-infection of COVID-19 and tuberculosis

Mekonen, Kassahun Getnet; Obsu, Legesse Lemecha

doi:10.1016/j.heliyon.2022.e11195

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Several fractional studies of deterministic, stochastic and incommensurate models on COVID-19 illness were delved in the literature examining various fear factors, diagnosed, threatened, awareness of pathogen spread, vaccination schemes, vaccine hesitancy, vaccine inefficacy, treatment, isolation, exposure to the virulent environment, effective vaccination with self-precautions, vaccine breakthrough infections, symptomatic and asymptomatic carriers and mutant strains, and so on. [30][31][32][33][34][35][36][37][38][39][40][41][42] COVID-19-related coinfections were studied with other superinfections namely hepatitis-B, 43 Tuberculosis, 44 and diabetes mellitus. 45 Optimal control modeling analysis were very effective in epidemical studies implementing effective control measures for disease annihilation found in References 46-50.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Motivated from the works of References 30,31 which explains the hesitation and threatening of virus, isolation effects, vaccination and environmental exposure, age factors, demographic changes, 32,33 symptomatic and asymptomatic carriers and mutations visualized in References 34,35, fear effects due to isolation and quarantine, vaccination, self‐precautionary measures, and associated coinfections studied in References 36–45. However, we present vaccine breakthrough infections, vaccine efficiency and microbial pathogen coinfections in a novel way on our proposed COVID‐19 coinfection model.…”

Section: Covid‐19 Coinfection Model Presentationmentioning

confidence: 99%

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Fractional commensurate model on COVID‐19 with microbial co‐infection: An optimal control analysis

Vijayalakshmi,

Roselyn Besi,

Akgül

2024

Optim Control Appl Methods

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Crossover behaviors have always existed in the history of infectious pandemics due to a few distinct, erratic spread outlines. This research aims to investigate the crossover behavior of the proposed SVICR commensurate fractional model for the COVID‐19 delta variant, considering microbial coinfections. A mathematical model in terms of Atangana–Baleanu Caputo (ABC) category fractional integrals takes into account the co‐infection of mucormycosis in immunocompromised COVID‐19 patients caused by microbial infections. ABC operators preserve the intact history of the happenings under contemplation through its nonsingular kernel. It is observed that the framed five‐compartmental SVICR model is positively bounded on R5, the solution space. Two equilibrium points representing the survival and annihilation of sickness respectively are contributed by the single population N(t), which is counted in five dependent compartments: The bilinear growth rate of new additional infections from the contagious infectives over time ‘t’ is viewed through the threshold metric Lyapunov's stability function examines the parametric influences over the virulent spread globally. The significant focus is to investigate the Mucormycosis cases in COVID‐19 patients with underlying diabetic complications. Diabetes mellitus is the major concern for several coinfections among COVID‐19 recoveries. Aiming to minimalize the critical states, an Lagrangian–Hamiltonian optimum control structure is also performed for the SVICR model by introducing control variables in effect to tri‐control probes of minimized contact rates, persuasive vaccinations, and glycemic control of post recovered diabetic patients. The hike in the Severity of the ailment due to fungal pathogens is studied through numerical convergence of predictor–corrector scheme and simulations. Using estimated parametric values from the statistical data of mucormycosis and infections of COVID‐19 reported cases in India, the prominence of control effects are visualized graphically. To conclude, a complete qualitative analysis of the minimization problem is executed for different levels of control values. We avow that effective control intrusions would almost certainly decline the complexities associated with the viral pathogens.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Covid‐19 Coinfection Model Presentationmentioning

confidence: 99%

Fractional commensurate model on COVID‐19 with microbial co‐infection: An optimal control analysis

Vijayalakshmi,

Roselyn Besi,

Akgül

2024

Optim Control Appl Methods

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“…From 2020 to 2022, some epidemiological models were developed to describe the betweenhost transmission of COVID-19 co-infection with other diseases such as bacteria/COVID-19 [46], tuberculosis/COVID-19 [47], Dengue/COVID-19 [48], Dengue/HIV/COVID-19 [49], ZIKV/COVID-19 [50], HIV/COVID-19 [51], and influenza/COVID-19 [52]. On the other hand, virological models that describe the in-host dynamics of SARS-CoV-2 with coinfection with other micro-organisms have been investigated in recent studies: influenza A virus/SARS-CoV-2 [53,54], malaria/SARS-CoV-2 [55], HIV/SARS-CoV-2 [56], and bacteria/ SARS-CoV-2 [57].…”

Section: Htlv-i and Sars-cov-2 Co-infection Modelmentioning

confidence: 99%

Global Dynamics of an HTLV-I and SARS-CoV-2 Co-Infection Model with Diffusion

et al. 2023

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel respiratory virus that causes coronavirus disease 2019 (COVID-19). Symptoms of COVID-19 range from mild to severe illness. It was observed that disease progression in COVID-19 patients depends on their immune response, especially in elderly patients whose immune system suppression may put them at increased risk of infection. Human T-cell lymphotropic virus type-I (HTLV-I) attacks the CD4+ T cells (T cells) of the immune system and leads to immune dysfunction. Co-infection with HTLV-I and SARS-CoV-2 has been reported in recent studies. Modeling HTLV-I and SARS-CoV-2 co-infection can be a helpful tool to understand the in-host co-dynamics of these viruses. The aim of this study was to construct a model that characterizes the in-host dynamics of HTLV-I and SARS-CoV-2 co-infection. By considering the mobility of the viruses and cells, the model is represented by a system of partial differential equations (PDEs). The system contains two independent variables, time t and position x, and seven dependent variables for representing the densities of healthy epithelial cells (ECs), latent SARS-CoV-2-infected ECs, active SARS-CoV-2-infected ECs, SARS-CoV-2, healthy T cells, latent HTLV-I-infected T cells and active HTLV-I-infected T cells. We first studied the fundamental properties of the solutions of the system, then deduced all steady states and proved their global properties. We examined the global stability of the steady states by constructing appropriate Lyapunov functions. The analytical results were illustrated by performing numerical simulations. We discussed the effect of HTLV-I infection on COVID-19 progression. The results suggest that patients with HTLV-I have a weakened immune response; consequently, their risk of COVID-19 infection may be increased.

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“…Such models can help to guide public health policies and provide methods for efficiently managing and preventing co-infections. Several confection models have been constructed, mathematically examined, and applied [1][2][3][4][5][6][7][8] , just to mention a few. Hepatitis C virus and HIV co-infection, although ineffectively comprehended, is a developing general wellbeing concern, essentially because of their nearby normal pathway relationship.…”

Section: Computational Study Of a Co-infection Model Of Hiv/aids And ...mentioning

confidence: 99%

Computational study of a co-infection model of HIV/AIDS and hepatitis C virus models

Dayan,

Ahmed,

Bariq

et al. 2023

Sci Rep

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Hepatitis C infection and HIV/AIDS contaminations are normal in certain areas of the world, and because of their geographic overlap, co-infection can’t be precluded as the two illnesses have a similar transmission course. This current work presents a co-infection model of HIV/AIDS and Hepatitis C virus with fuzzy parameters. The application of fuzzy theory aids in tackling the issues associated with measuring uncertainty in the mathematical depiction of diseases. The fuzzy reproduction number and fuzzy equilibrium points have been determined in this context, focusing on a model applicable to a specific group defined by a triangular membership function. Furthermore, for the model, a fuzzy non-standard finite difference (NSFD) technique has been developed, and its convergence is examined within a fuzzy framework. The suggested model is numerically validated, confirming the dependability of the devised NSFD technique, which successfully retains all of the key properties of a continuous dynamical system.

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Mathematical modeling and analysis for the co-infection of COVID-19 and tuberculosis

Cited by 19 publications

References 28 publications

Fractional commensurate model on COVID‐19 with microbial co‐infection: An optimal control analysis

Fractional commensurate model on COVID‐19 with microbial co‐infection: An optimal control analysis

Global Dynamics of an HTLV-I and SARS-CoV-2 Co-Infection Model with Diffusion

Computational study of a co-infection model of HIV/AIDS and hepatitis C virus models

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